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Challenge for Diagnostic Assessment of Deep Learning Algorithm for Metastases Classification in Sentinel Lymph Nodes on Frozen Tissue Section Digital Slides in Women with Breast Cancer

PURPOSE: Assessing the status of metastasis in sentinel lymph nodes (SLNs) by pathologists is an essential task for the accurate staging of breast cancer. However, histopathological evaluation of SLNs by a pathologist is not easy and is a tedious and time-consuming task. The purpose of this study is...

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Autores principales: Kim, Young-Gon, Song, In Hye, Lee, Hyunna, Kim, Sungchul, Yang, Dong Hyun, Kim, Namkug, Shin, Dongho, Yoo, Yeonsoo, Lee, Kyowoon, Kim, Dahye, Jung, Hwejin, Cho, Hyunbin, Lee, Hyungyu, Kim, Taeu, Choi, Jong Hyun, Seo, Changwon, Han, Seong il, Lee, Young Je, Lee, Young Seo, Yoo, Hyung-Ryun, Lee, Yongju, Park, Jeong Hwan, Oh, Sohee, Gong, Gyungyub
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Korean Cancer Association 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7577824/
https://www.ncbi.nlm.nih.gov/pubmed/32599974
http://dx.doi.org/10.4143/crt.2020.337
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author Kim, Young-Gon
Song, In Hye
Lee, Hyunna
Kim, Sungchul
Yang, Dong Hyun
Kim, Namkug
Shin, Dongho
Yoo, Yeonsoo
Lee, Kyowoon
Kim, Dahye
Jung, Hwejin
Cho, Hyunbin
Lee, Hyungyu
Kim, Taeu
Choi, Jong Hyun
Seo, Changwon
Han, Seong il
Lee, Young Je
Lee, Young Seo
Yoo, Hyung-Ryun
Lee, Yongju
Park, Jeong Hwan
Oh, Sohee
Gong, Gyungyub
author_facet Kim, Young-Gon
Song, In Hye
Lee, Hyunna
Kim, Sungchul
Yang, Dong Hyun
Kim, Namkug
Shin, Dongho
Yoo, Yeonsoo
Lee, Kyowoon
Kim, Dahye
Jung, Hwejin
Cho, Hyunbin
Lee, Hyungyu
Kim, Taeu
Choi, Jong Hyun
Seo, Changwon
Han, Seong il
Lee, Young Je
Lee, Young Seo
Yoo, Hyung-Ryun
Lee, Yongju
Park, Jeong Hwan
Oh, Sohee
Gong, Gyungyub
author_sort Kim, Young-Gon
collection PubMed
description PURPOSE: Assessing the status of metastasis in sentinel lymph nodes (SLNs) by pathologists is an essential task for the accurate staging of breast cancer. However, histopathological evaluation of SLNs by a pathologist is not easy and is a tedious and time-consuming task. The purpose of this study is to review a challenge competition (HeLP 2018) to develop automated solutions for the classification of metastases in hematoxylin and eosin–stained frozen tissue sections of SLNs in breast cancer patients. MATERIALS AND METHODS: A total of 297 digital slides were obtained from frozen SLN sections, which include post–neoadjuvant cases (n = 144, 48.5%) in Asan Medical Center, South Korea. The slides were divided into training, development, and validation sets. All of the imaging datasets have been manually segmented by expert pathologists. A total of 10 participants were allowed to use the Kakao challenge platform for 6 weeks with two P40 GPUs. The algorithms were assessed in terms of the area under receiver operating characteristic curve (AUC). RESULTS: The top three teams showed 0.986, 0.985, and 0.945 AUCs for the development set and 0.805, 0.776, and 0.765 AUCs for the validation set. Micrometastatic tumors, neoadjuvant systemic therapy, invasive lobular carcinoma, and histologic grade 3 were associated with lower diagnostic accuracy. CONCLUSION: In a challenge competition, accurate deep learning algorithms have been developed, which can be helpful in making frozen diagnosis of intraoperative SLN biopsy. Whether this approach has clinical utility will require evaluation in a clinical setting.
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spelling pubmed-75778242020-10-26 Challenge for Diagnostic Assessment of Deep Learning Algorithm for Metastases Classification in Sentinel Lymph Nodes on Frozen Tissue Section Digital Slides in Women with Breast Cancer Kim, Young-Gon Song, In Hye Lee, Hyunna Kim, Sungchul Yang, Dong Hyun Kim, Namkug Shin, Dongho Yoo, Yeonsoo Lee, Kyowoon Kim, Dahye Jung, Hwejin Cho, Hyunbin Lee, Hyungyu Kim, Taeu Choi, Jong Hyun Seo, Changwon Han, Seong il Lee, Young Je Lee, Young Seo Yoo, Hyung-Ryun Lee, Yongju Park, Jeong Hwan Oh, Sohee Gong, Gyungyub Cancer Res Treat Original Article PURPOSE: Assessing the status of metastasis in sentinel lymph nodes (SLNs) by pathologists is an essential task for the accurate staging of breast cancer. However, histopathological evaluation of SLNs by a pathologist is not easy and is a tedious and time-consuming task. The purpose of this study is to review a challenge competition (HeLP 2018) to develop automated solutions for the classification of metastases in hematoxylin and eosin–stained frozen tissue sections of SLNs in breast cancer patients. MATERIALS AND METHODS: A total of 297 digital slides were obtained from frozen SLN sections, which include post–neoadjuvant cases (n = 144, 48.5%) in Asan Medical Center, South Korea. The slides were divided into training, development, and validation sets. All of the imaging datasets have been manually segmented by expert pathologists. A total of 10 participants were allowed to use the Kakao challenge platform for 6 weeks with two P40 GPUs. The algorithms were assessed in terms of the area under receiver operating characteristic curve (AUC). RESULTS: The top three teams showed 0.986, 0.985, and 0.945 AUCs for the development set and 0.805, 0.776, and 0.765 AUCs for the validation set. Micrometastatic tumors, neoadjuvant systemic therapy, invasive lobular carcinoma, and histologic grade 3 were associated with lower diagnostic accuracy. CONCLUSION: In a challenge competition, accurate deep learning algorithms have been developed, which can be helpful in making frozen diagnosis of intraoperative SLN biopsy. Whether this approach has clinical utility will require evaluation in a clinical setting. Korean Cancer Association 2020-10 2020-06-30 /pmc/articles/PMC7577824/ /pubmed/32599974 http://dx.doi.org/10.4143/crt.2020.337 Text en Copyright © 2020 by the Korean Cancer Association This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Article
Kim, Young-Gon
Song, In Hye
Lee, Hyunna
Kim, Sungchul
Yang, Dong Hyun
Kim, Namkug
Shin, Dongho
Yoo, Yeonsoo
Lee, Kyowoon
Kim, Dahye
Jung, Hwejin
Cho, Hyunbin
Lee, Hyungyu
Kim, Taeu
Choi, Jong Hyun
Seo, Changwon
Han, Seong il
Lee, Young Je
Lee, Young Seo
Yoo, Hyung-Ryun
Lee, Yongju
Park, Jeong Hwan
Oh, Sohee
Gong, Gyungyub
Challenge for Diagnostic Assessment of Deep Learning Algorithm for Metastases Classification in Sentinel Lymph Nodes on Frozen Tissue Section Digital Slides in Women with Breast Cancer
title Challenge for Diagnostic Assessment of Deep Learning Algorithm for Metastases Classification in Sentinel Lymph Nodes on Frozen Tissue Section Digital Slides in Women with Breast Cancer
title_full Challenge for Diagnostic Assessment of Deep Learning Algorithm for Metastases Classification in Sentinel Lymph Nodes on Frozen Tissue Section Digital Slides in Women with Breast Cancer
title_fullStr Challenge for Diagnostic Assessment of Deep Learning Algorithm for Metastases Classification in Sentinel Lymph Nodes on Frozen Tissue Section Digital Slides in Women with Breast Cancer
title_full_unstemmed Challenge for Diagnostic Assessment of Deep Learning Algorithm for Metastases Classification in Sentinel Lymph Nodes on Frozen Tissue Section Digital Slides in Women with Breast Cancer
title_short Challenge for Diagnostic Assessment of Deep Learning Algorithm for Metastases Classification in Sentinel Lymph Nodes on Frozen Tissue Section Digital Slides in Women with Breast Cancer
title_sort challenge for diagnostic assessment of deep learning algorithm for metastases classification in sentinel lymph nodes on frozen tissue section digital slides in women with breast cancer
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7577824/
https://www.ncbi.nlm.nih.gov/pubmed/32599974
http://dx.doi.org/10.4143/crt.2020.337
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